A model–data fusion approach for predicting cover crop nitrogen supply to corn

One potential benefit of cover crops (CCs) is that N mineralization from decomposing CC residues may reduce the N fertilizer requirement of a subsequent crop, but predicting this credit remains a significant challenge. This study used a model–data fusion approach to calibrate a model of CC residue N mineralization and pre-emptive competition for soil NO₃^– that occurs during CC growth to predict the yield response of an unfertilized corn (Zea mays L.) crop. The model was calibrated with a data set of 199 observations from four CC experiments in central Pennsylvania. The most parsimonious model explained 82% of the variation in corn yield response. Parameters representing the C humification coefficients for decomposed residues from winterkilled (ε_wk = 0.00) and winter-hardy (ε_wh = 0.40) CCs suggest that all winterkilled CCs resulted in net N mineralization, probably due to the longer period of time for decomposition of winterkilled residues. However, the yield response per unit of potentially mineralized N was greater for winter-hardy CCs (α_wh = 0.034 with tillage, α_wh = 0.020 with no-till) than for winterkilled CCs (α_wk = 0.0084), probably due to the improved synchrony between corn N demand and the decomposition of winter-hardy CC residues relative to winterkilled residues. Pre-emptive competition for soil NO₃^– led to a reduction in the corn yield response. Because the model is based on ecological processes and can be calibrated with data sets from simple field experiments, the model–data fusion approach could be widely used to guide adaptive management of CCs and N fertilizer applications in a subsequent corn crop.